class_docstr: A LayerNorm variant, popularized by Transformers, that performs point-wise mean and
  variance normalization over the channel dimension for inputs that have shape
  (batch_size, channels, height, width).
  https://github.com/facebookresearch/ConvNeXt/blob/d1fa8f6fef0a165b27399986cc2bdacc92777e40/models/convnext.py#L119  # noqa B950
code: def __init__(self, normalized_shape, eps=1e-6):
      super().__init__()
      self.weight = nn.Parameter(torch.ones(normalized_shape))
      self.bias = nn.Parameter(torch.zeros(normalized_shape))
      self.eps = eps
      self.normalized_shape = (normalized_shape,)
  
imports: import torch
  import torch.distributed
  from fvcore.nn.distributed import differentiable_all_reduce
  from torch import nn
  from torch.nn import functional
  from detectron2.utils import comm, env
  from .wrappers import BatchNorm2d
label_desc: Use image node_id 1 to create a new LayerNorm object from inherited base classes: nn with example: obj = LayerNorm(normalized_shape, eps)